Description Usage Arguments Details Value See Also
Run simple indirect meta-analysis for all possible pairwise comparisons in a dataset
1 2 | runIndirect(df, data_type, direct_results, continuous = FALSE,
effect_type = "all", back_calc = FALSE, order_treatments = NA)
|
df |
A |
data_type |
A character string specifying which type of data has been provided. Currently only 'treatment difference' or 'binary' are supported |
direct_results |
A data frame containing the results of direct meta-analysis. These results are required to provide the inputs for the indirect comparisons. The best way to produce this is to use the tools for direct meta-analysis from this package |
continuous |
Logical (TRUE/FALSE) indicating whether the effect measure
is continuous (mean difference) or a ratio measure (odds ratio, hazard
ratio etc). This is passed directly to |
effect_type |
A character string indicating what kind of analysis is required. Set to 'Fixed' for fixed effect, 'Random' for random effects or 'all' to get both (Default). |
back_calc |
A logical indicating whether results should be back transformed.
This is used to set the corresponding |
order_treatments |
An optional argument to specify the order in which treatment comparisons are sorted in the output. The default is NA in which case comparisons will be sorted alphabetically by intervention. If a specific order is required then this should be provided as a data frame with two columns named 'description' and 'Order'. Note that column headers are specific and case sensitive. The description column should contain the names of the treatments exactly as they are specified in the data set. The id column should contain the numbered order of treatments required. |
This function performs indirect meta-analysis using the Bucher
method for all possible comparisons in a given data set. This function
takes a set of treatment comparisons from one or more studies and
identifies all possible indirect comparisons where two treatments can be
connected via a common comparator. If there is more than one way to
connect two treatments then all possible variations are calculated. This
function calls doBucher
internally to calculate the
treatment effects
The inputs for this function will usually be the results from direct meta-analysis for a given set of treatments. The recommended workflow is to use the tools from this package to perform head to head meta-analysis for a given set of treatments then use the resulting data frame to provide the inputs for this function.
A data frame containing the results of all possible indirect
comparisons in the data set. The help page for doBucher
provides a detailed description of the columns in the output
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